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Authors: Yiqiang Chen 1 ; Stefan Duffner 1 ; Andrei Stoian 2 ; Jean-Yves Dufour 2 and Atilla Baskurt 1

Affiliations: 1 Université de Lyon, France ; 2 Thales Services, France

ISBN: 978-989-758-290-5

Keyword(s): Pedestrian Attributes, Convolutional Neural Networks, Multi-label Classification.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Motion, Tracking and Stereo Vision ; Video Surveillance and Event Detection

Abstract: In video surveillance, pedestrian attributes such as gender, clothing or hair types are useful cues to identify people. The main challenge in pedestrian attribute recognition is the large variation of visual appearance and location of attributes due to different poses and camera views. In this paper, we propose a neural network combining high-level learnt Convolutional Neural Network (CNN) features and low-level handcrafted features to address the problem of highly varying viewpoints. We first extract low-level robust Local Maximal Occurrence (LOMO) features and learn a body part-specific CNN to model attribute patterns related to different body parts. For small datasets which have few data, we propose a new learning strategy, where the CNN is pre-trained in a triplet structure on a person re-identification task and then fine-tuned on attribute recognition. Finally, we fuse the two feature representations to recognise pedestrian attributes. Our approach achieves state-of-the-art resul ts on three public pedestrian attribute datasets. (More)

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Paper citation in several formats:
Chen, Y.; Duffner, S.; Stoian, A.; Dufour, J. and Baskurt, A. (2018). Pedestrian Attribute Recognition with Part-based CNN and Combined Feature Representations.In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5 VISAPP: VISAPP, ISBN 978-989-758-290-5, pages 114-122. DOI: 10.5220/0006622901140122

@conference{visapp18,
author={Yiqiang Chen. and Stefan Duffner. and Andrei Stoian. and Jean{-}Yves Dufour. and Atilla Baskurt.},
title={Pedestrian Attribute Recognition with Part-based CNN and Combined Feature Representations},
booktitle={Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5 VISAPP: VISAPP,},
year={2018},
pages={114-122},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006622901140122},
isbn={978-989-758-290-5},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5 VISAPP: VISAPP,
TI - Pedestrian Attribute Recognition with Part-based CNN and Combined Feature Representations
SN - 978-989-758-290-5
AU - Chen, Y.
AU - Duffner, S.
AU - Stoian, A.
AU - Dufour, J.
AU - Baskurt, A.
PY - 2018
SP - 114
EP - 122
DO - 10.5220/0006622901140122

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